optimization of light interception, leaf area and yield in “wa38”: comparisons among training systems, rootstocks and pruning techniques

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ID: 136616
2020
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Abstract
As apple orchards have transitioned to high-density plantings, proper training systems are required to manage increased leaf area. Leaf area index (LAI) is defined as the ratio between leaf area to ground area (m2/m2) and can infer orchard health, light relationships and productivity. New technologies enable rapid assessments of LAI and light interception (LI) in the orchard. In this study, LAI, LI, and productivity were assessed across two training systems (Spindle and V), two rootstocks (Geneva 41® (G41) and Malling 9—Nic29 (Nic29)) and two pruning techniques (“click” and bending) in 2016 and 2017. The objective of this study was to determine a management strategy for “WA38” to meet optimal levels for LAI (1.2–2.0) and light interception (65–75%). Higher light interception was measured in V compared to Spindle and in G41 compared to Nic29 in both years. Minimal differences in LAI and light interception were detected across pruning techniques. In “WA38” the “click” technique maintained more consistent yields than bending. In both years, the Spindle-Nic29-“click” combination maintained optimal thresholds for LAI (1.93 and 1.48), light interception (66% and 68%) and consistent yields. This sequence helps mitigate “blind wood” and alternate bearing, while optimizing leaf area and light in “WA38”.
Reference Key
anthony2020agronomyoptimization Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Brendon Anthony;Sara Serra;Stefano Musacchi
Journal drinking water engineering and science
Year 2020
DOI
10.3390/agronomy10050689
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